title: Ultrasound contrast image segmentation using a modified level set method.
abstract: Manual segmentation of ultrasound contrast images is time-consuming and inevitable to variability, and computer-based segmentation algorithms often require user interaction. This paper proposes a novel level set model for fully automated segmentation of vascular ultrasound contrast images. The initial contour of arterial boundaries is acquired based on an automatic procedure. The level set model moves the initial contour towards the boundaries of arterial inner wall based on minimization of the energy function. The traditional energy function is improved by introducing an edge detector based on image gradient and the standard difference image. Both spatial and temporal information of the image are considered, and the robustness and accuracy of the level set model is enhanced. Ultrasonic contrast images of living mouse are acquitted with high frequency ultrasound system. Images of carotid arteries are processed with our method. The segmentation results using the proposed method are evaluated against two observers' hand-outlined boundaries, showing that computer-generated boundaries agree well with the observers' hand-outlined boundaries as much as the different observers agree with each other.
authors.forename: Ming
authors.initials: M
authors.lastname: Qian
authors.forename: Lili
authors.initials: L
authors.lastname: Niu
authors.forename: Yang
authors.initials: Y
authors.lastname: Xiao
authors.forename: Congzhi
authors.initials: C
authors.lastname: Wang
authors.forename: Weibao
authors.initials: W
authors.lastname: Qiu
authors.forename: Hairong
authors.initials: H
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D000818:Animals; D002339:Carotid Arteries / Q000000981:diagnostic imaging*; D003287:Contrast Media / Q000737:chemistry; D006439:Hemodynamics; D019220:High-Energy Shock Waves; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D051379:Mice; D008962:Models, Theoretical; D016011:Normal Distribution; D015588:Observer Variation; D010363:Pattern Recognition, Automated; D014463:Ultrasonography
chemical_list: D003287:Contrast Media